Forgiveness, spirituality and love: thematic analysis of last statements from Death Row, Texas (2002-17)

Is lifetime abuse forgivable in old age?

Old age is characterized by reflection and a retrospective examination of the multiple meanings of various life experiences, including lifelong abuse. Forgiveness is found to have a salutary effect, especially for older adults. To understand the place and role of forgiveness in the reflective process during aging, we performed a secondary analysis of in-depth, semi-structured interviews (N = 78) with older women survivors of abuse. Inductive thematic analysis was based on concepts developed deductively from the literature review. The findings include three main themes: (1) The dimensions of forgiving: The victim as subject; (2) Being forgiven: Between lost forgiveness and hope; and (3) Self-forgiveness and the aging self. Despite the known salutary effect of forgiveness, we must consider that this is not a universally desirable process. We included the dimension of forgiveness in the study of abuse throughout the older person's life course and identified further complexities in addition to the "forgiveness"/"unforgiveness."

Dehumanization Through Degendering the Death Row Inmate: A Systematic Review of the Research

Purpose: To provide an overview of how gender identity is treated in death row research. Methods: By use of a systematic review of 56 peer-reviewed journal articles that were identified as empirical, employing either qualitative or quantitative data, concepts measuring the use of gender and race identity were developed. Results: Findings were presented by the methodology employed, area of research, sample composition, and key concepts which included the use of gendered terminology in titles, abstracts, and manuscripts, as well as by the author(s)'s gender identities. Conclusion: In general, regardless of methodology or area of research, death row research does not account for the effects of gender or intersectional identities unless the sample participants or subject matter pertains to women.

Automatic identification of suicide notes with a transformer-based deep learning model

Suicide is one of the leading causes of death worldwide. At the same time, the widespread use of social media has led to an increase in people posting their suicide notes online. Therefore, designing a learning model that can aid the detection of suicide notes online is of great importance. However, current methods cannot capture both local and global semantic features. In this paper, we propose a transformer-based model named TransformerRNN, which can effectively extract contextual and long-term dependency information by using a transformer encoder and a Bi-directional Long Short-Term Memory (BiLSTM) structure. We evaluate our model with baseline approaches on a dataset collected from online sources (including 659 suicide notes, 431 last statements, and 2000 neutral posts). Our proposed TransformerRNN achieves 95.0%, 94.9% and 94.9% performance in P, R and F1-score metrics respectively and therefore outperforms comparable machine learning and state-of-the-art deep learning models. The proposed model is effective for classifying suicide notes, which in turn, may help to develop suicide prevention technologies for social media.